首页|Deep knowledge integration of heterogeneous features for domain adaptive SAR target recognition
Deep knowledge integration of heterogeneous features for domain adaptive SAR target recognition
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
How to integrate various heterogeneous features for better recognition performance is increasingly critical for automatic target recognition. Existing integration methods present the following drawbacks: (1) most feature integration methods ignore the information, both common and discriminate knowledge, among different types of features; (2) most decision integration methods ignore the fact that different knowledge contributes differently; (3) the feature weights of integration model learned in the source domain cannot perform well in the target domain. To tackle these problems, we propose a deep Knowledge Integration framework by combining heterogeneous features for Domain Adaptive synthetic aperture radar (SAR) target recognition (KIDA). In the training phase, we implement deep knowledge integration at both feature and decision levels. At the feature level, to exploit the common and discriminative knowledge, multiple heterogeneous features are projected from the feature space into a unified label space by exploring the shared and specific structures simultaneously. The shared structure integrates common information in different features, while the specific structure reserves discriminative information of each type of feature. At the decision level, to reveal the relative importance of different knowledge, a decision integration strategy with feature weights is adopted in the label space. In the online testing phase, to improve the generalization of the model in dynamical environments, we employ online learning with sequential target domain knowledge to update the feature weights, thus achieving domain adaptation. Extensive experiments on different datasets validate the effectiveness and advantages of the proposed KIDA, especially in noisy environments. (c) 2022 Elsevier Ltd. All rights reserved.
NSTL
Elsevier
